Deep Learning-based Color Compensation Method for Projected Images in Combat Assistant System

被引：0
|
作者：
Zhang F. [1 ,2 ]
Zhang C. [1 ]
Yang H. [1 ]
Wang F. [1 ,2 ]
机构：
[1] College of Computer Science and Technology, Changchun University of Science and Technology, Jilin
[2] College of Computer, Jilin Normal University, Jilin
来源：
Zhang, Chao (zhangchao@cust.edu.cn) | 1600年 / China Ordnance Industry Corporation卷 / 42期
关键词：
Color compensation; Combat assistant system; Deep learning; Projected image;
D O I：
10.3969/j.issn.1000-1093.2021.11.015
中图分类号：
学科分类号：
摘要：
A deep learning-based color compensation method for projected images is proposed to realize the visual display of large-scale data in arbitrary complex color plane. The proposed method is aimed to realize the rapid deployment of display system in complex environment and improve the mobility of field command system in modern war. The method is used to avoid the inherent defects of multiple modules in deep learning based on an end-to-end deep learning network, a model and an objective function. The number of image features extracted from the training model is increased by deepening the layers of CompenNet. The improved loss function SSIM+Smooth L1 is used to calculate image similarity, which enhances the robustness and stability of the loss function while speeding up the convergence of the network. The experimental results show that the PSNR and SSIM average values of the images generated by the improved CompenNet after 1 000 training iterations on the same 24 data sets are increased by 5.54% and 0.14%, respectively, the RMSE average value is decreased by 0.14%, and the subjective perception projection effect of human eyes is also better. © 2021, Editorial Board of Acta Armamentarii. All right reserved.
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页码：2418 / 2423
页数：5
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